A Study on Enhanced Spatial Clustering Using Ensemble Dbscan and Umap to Map Fire Zone in Greater Jakarta, Indonesia


  • Silviya Hasana Bina Nusantara University
  • Devi Fitrianah Bina Nusantara University
(*) Corresponding Author




dbscan, clustering, ensemble, fires, umap


This research investigated ensemble clustering algorithms and dimensionality reduction for fire zone mapping, specifically DBSCAN + UMAP. We evaluated six clustering methods: DBSCAN, ensemble DBSCAN, DBSCAN + UMAP, ensemble DBSCAN + UMAP, HDBSCAN and Gaussian Mixture Model (GMM). We evaluated our results based on the Silhouette Score and the Davies-Bouldin (DB) index, emphasizing handling irregular cluster shapes, smaller clusters and resolving incompact clusters. Our findings suggested that ensemble DBSCAN + UMAP outperformed five other methods with zero noise clusters indicating clustering results are resistant to outliers, leading to a clearer identification of fire-prone areas, a high Silhouette Score of 0.971, indicating accurate cluster separation of distinct areas of potential fire hazards and an exceptionally low DB Index of 0.05 that indicates compact clusters to identify well-defined and geographically concentrated areas prone to fire hazards. Our findings contribute to the advanced techniques for minimizing the impacts of fires and improving fire hazard assessments in Indonesia.


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Author Biographies

Silviya Hasana, Bina Nusantara University

Computer Science Program, School of Computer Science

Devi Fitrianah, Bina Nusantara University

Computer Science Department, Binus Graduate Program, Master of Computer Science


Abid, F. (2021). A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems. Fire Technology, 57(2), 559–590. https:// doi.org/10.1007/s10694-020-01056-z

Courtwright, J. (2023). Prairie Fire: A Great Plains History. University Press of Kansas.

Leiras, A. B., Rodrigues, J. P. C., & Meacham, B. J. (2021). A performance-based fire risk analysis for buildings. Architecture, Structures and Construction, 1(2), 143–175.https://doi.org/10.1007/s44150-021-00016-7

Harrison, M. E., Ottay, J. B., D’Arcy, L. J., Cheyne, S. M., Anggodo, Belcher, C., Cole, L., Dohong, A., Ermiasi, Y., Feldpausch, T., Gallego-Sala, A., Gunawan, A., Höing, A., Husson, S. J., Kulu, I. P., Soebagio, S. M., Mang, S., Mercado, L., Morrogh-Bernard, H. C., … van Veen, F. J. F. (2020). Tropical forest and peatland conservation in Indonesia: Challenges and directions. People and Nature, 2(1), 4–28. https://doi.org/10.1002/pan3.10060

Brasika, I. B. M. (2023). Forest Fire Emissions in Equatorial Asia and Their Recent Delay Anomaly in the Dry Season. In K. P. Vadrevu, T. Ohara, & C. Justice (Eds.), Vegetation Fires and Pollution in Asia (pp. 447–462). Springer International Publishing.https://doi.org/10.1007/978-3-031-29916-2_26

Lin, S., Cheung, Y. K., Xiao, Y., & Huang, X. (2020). Can Rain Suppress Smoldering Peat Fire? Science of The Total Environment, 727, 138468. https://doi.org/10.1016/j.scitotenv.2020.138468

Nurdiati, S., Bukhari, F., Julianto, M. T., Sopaheluwakan, A., Aprilia, M., Fajar, I., Septiawan, P., & Najib, M. K. (2022). The impact of El Niño Southern Oscillation and Indian Ocean Dipole on the Burned Area in Indonesia. Terrestrial, Atmospheric and Oceanic Sciences, 33(1), 16.https://doi.org/10.1007/s44195-022-00016-0

Rahardjo, H. A., & Prihanton, M. (2020). The Most Critical Issues and Challenges of Fire Safety for Building Sustainability in Jakarta. Journal of Building Engineering, 29, 101133. https://doi.org/10.1016/ j.jobe.2019.101133

Murugesan, N., Cho, I., & Tortora, C. (2021). Benchmarking in Cluster Analysis: A Study on Spectral Clustering, DBSCAN, and K-Means. In T. Chadjipadelis, B. Lausen, A. Markos, T. R. Lee, A. Montanari, & R. Nugent (Eds.), Data Analysis and Rationality in a Complex World. Springer International Publishing. https://doi.org/10.1007/ 978-3-030-60104-1_20

Artés, T., Oom, D., de Rigo, D., Durrant, T. H., Maianti, P., Libertà, G., & San-Miguel-Ayanz, J. (2019). A Global Wildfire Dataset for the Analysis of Fire Regimes and Fire Behaviour. Scientific Data, 6(1), Article 1. https://doi.org/ 10.1038/s41597-019-0312-2

Ester, M., Kriegel, H., Sander, J., & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Knowledge Discovery and Data Mining (Vol. 96, pp. 226–231).

Campello, R. J., Moulavi, D., Zimek, A., & Sander, J. (2015). Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Transactions on Knowledge Discovery from Data (TKDD), 10(1), 1-51.

Huang, J., Xu, Z., Yang, F., Zhang, W., Cai, S., Luo, J., Xie, G., & Li, T. (2022). Fire Risk Assessment and Warning Based on Hierarchical Density-Based Spatial Clustering Algorithm and Grey Relational Analysis. Mathematical Problems in Engineering, 2022. https:// doi.org/10.1155/2022/7339312

Zerbe, K., Polit, C., McClain, S., & Cook, T. (2022). Optimized Hot Spot and Directional Distribution Analyses Characterize the Spatiotemporal Variation of Large Wildfires in Washington, USA, 1970−2020. International Journal of Disaster Risk Science, 13(1), 139–150. https:// doi.org/10.1007/s13753-022-00396-4

Júnior, J. S. S., Paulo, J. R., Mendes, J., Alves, D., Ribeiro, L. M., & Viegas, C. (2022). Automatic Forest Fire Danger Rating Calibration: Exploring clustering techniques for regionally customizable fire danger classification. Expert Systems with Applications, 193, 116380. https://doi.org/10.1016/j.eswa.2021.116380

McInnes, L., Healy, J., & Melville, J. (2020). (2018). UMAP: Uniform Manifold Approximation and Projection. Journal of Open Source Software, 3(29), 861, https://doi.org/10.21105/joss.00861

Luo, J., Han, Y., Zhao, Y., Huang, Y., Liu, X., Tao, S., Liu, J., Huang, T., Wang, L., Chen, K., & Ma, J. (2020). Effect of northern boreal forest fires on PAH fluctuations across the arctic. Environmental Pollution, 261, 114186.https://doi.org/10.1016/j.envpol.2020.114186

St. Denis, L. A., Mietkiewicz, N. P., Short, K. C., Buckland, M., & Balch, J. K. (2020). All-hazards dataset mined from the US National Incident Management System 1999–2014. Scientific data, 7(1), 64.

Liang, H., Zhang, M., & Wang, H. (2019). A Neural Network Model for Wildfire Scale Prediction Using Meteorological Factors. IEEE Access, 7, 176746-176755.

Arellano-del-Verbo, G., Urbieta, I. R., & Moreno, J. M. (2023). Large-Fire Ignitions Are Higher in Protected Areas than Outside Them in West-Central Spain. Fire, 6(1), 28.

Mollaian, M. (2021). Application of Dimension Reduction and Clustering Methods for Detection of Faulty Operations in Process Systems. University of California, Davis.




How to Cite

Hasana, S., & Fitrianah, D. (2023). A Study on Enhanced Spatial Clustering Using Ensemble Dbscan and Umap to Map Fire Zone in Greater Jakarta, Indonesia. Jurnal Riset Informatika, 5(3), 409–418. https://doi.org/10.34288/jri.v5i3.234